• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.5
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• pp.527-547
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• 2009

Moving object management is widely used in traffic, logistic and data mining applications in ubiquitous environments. It is required to analyze spatio-temporal data and trajectories for moving object management. In this paper, we proposed a novel index structure for spatio-temporal aggregation of trajectory in a constrained network, named aCN-RB-tree. It manages aggregation values of trajectories using a constraint network-based index and it also supports direction of trajectory. An aCN-RB-tree consists of an aR-tree in its center and an extended B-tree. In this structure, an aR-tree is similar to a Min/Max R-tree, which stores the child nodes' max aggregation value in the parent node. Also, the proposed index structure is based on a constrained network structure such as a FNR-tree, so that it can decrease the dead space of index nodes. Each leaf node of an aR-tree has an extended B-tree which can store timestamp-based aggregation values. As it considers the direction of trajectory, the extended B-tree has a structure with direction. So this kind of aCN-RB-tree index can support efficient search for trajectory and traffic zone. The aCN-RB-tree can find a moving object trajectory in a given time interval efficiently. It can support traffic management systems and mining systems in ubiquitous environments.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.463-475
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• 2006

In applications such as a traffic management system, analysis using a spatial hierarchy of a spatial data warehouse and a simple aggregation is required. Over the past few years, several studies have been made on solution using a spatial index. Many studies have focused on using extended R-tree. But, because it just provides either the current aggregation or the total aggregation, decision support of traffic policy required historical analysis can not be provided. This paper proposes hybrid index based on extended aR-tree for the spatio-temporal aggregation. The proposed method supports a spatial hierarchy and the current aggregation by the R-tree. The sorted hash table using the time structure of the extended aR-tree provides a temporal hierarchy and a historical aggregation. Therefore, the proposed method supports an efficient decision support with spatio-temporal analysis and is Possible currently traffic analysis and determination of a traffic policy with historical analysis.

• Journal of Korea Multimedia Society
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• v.10 no.3
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• pp.358-364
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• 2007

This paper suggests MPLS restoration method that use an aggregation tree. For the restoration of the MPLS network, this paper presents a scheme that reconstruct multiple path through aggregation tree that grow from many node to a target node. Contrary to a multicast tree where a message that send at one node is passed to a group of node, an aggregation tree is a tree that data is passed from many node to one destination node. Through an experiment we confirmed that the restoration scheme that uses aggregation tree shows taster speed comparing with the existing other restoration scheme.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.212-213
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• 2015

Data aggregation is a fundamental problem in wireless sensor networks that has attracted great attention in recent years. On constructing a robust algorithm for minimizing data aggregation delay in wireless sensor networks, we consider limited transmission range sensors and approximate the minimum-delay data aggregation tree which can only be built in networks of unlimited transmission range sensors. The paper proposes an adaptive method that can be applied to maintain the network structure in case of a sensor node fails. The data aggregation tree built by the proposed scheme is therefore self-healing and robust. Intensive simulations are carried out and the results show that the scheme could adapt well to network topology changes compared with other approaches.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1057-1059
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• 2016

A construction method of energy-efficient data aggregation tree is proposed by considering a tradeoff between acquisition time and energy consumption in wireless sensor networks. This proposed method constructs the data aggregation tree to minimize the link cost between the connected nodes for reducing energy consumption, while minimizing the maximum distance between sensor nodes and a sink node for rapid information gathering. Simulation results show that the proposed aggregation tree can be generated with low complexity and achieves high energy efficiency compared to conventional methods.

• Journal of KIISE:Databases
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• v.29 no.6
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• pp.429-440
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• 2002

Temporal databases manage time-evolving data. They provide built-in supports for efficient recording and querying of temporal data. The temporal aggregate in temporal databases is an extension of the conventional aggregate to include time concept on the domain and range of aggregation. This paper focuses on multidimensional temporal aggregation. In a multidimensional temporal aggregate, we use one or more general attributes as well as a time attribute on the range of aggregation, thus it is a useful operation for historical data warehouse, Call Data Records(CDR), etc. In this paper, we propose a structure for multidimensional temporal aggregation, called PTA-tree, and an aggregate processing method based on the PTA-tree. Through analyses and performance experiments, we also compare the PTA-tree with the simple extension of SB-tree that was proposed for temporal aggregation.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.175-177
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• 2020

In multi-channel wireless sensor networks, optimizing data aggregation delay without any channels and timeslots conflicts has been concerned these days. The aggregation delay can be reduced by using different aggregation tree construction methods or scheduling in different methods in bottom-up or top-down manners. In this paper, we propose a new way of constructing aggregation tree purposing to decrease the total aggregation delay. The result shows that our proposed scheme can improve up to 64% comparing with state-of-the-art schemes.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.677-679
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• 2017

Data convergecast is an indispensable task for any WSN applications. Typically, scheduling in the WSN consists of two phases: tree construction and scheduling. The optimal tree structure and scheduling for the network is proven NP-hard. This paper focuses on the delay optimality while constructing the data convergecast tree. The algorithm can take any tree as the input, and by performing the switches (i.e. a node changes its parent), the expected aggregation delay is potentially reduced. Note that while constructing the tree, only the in-tree collisions between the child nodes sending data to their common parent is considered.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1529-1541
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• 2006

Moving objects have been widely employed in traffic and logistic applications. Spatio-temporal aggregations mainly describe the moving object's behavior in the spatial data warehouse. The previous works usually express the object moving in some certain region, but ignore the object often moving along as the trajectory. Other researches focus on aggregation and comparison of trajectories. They divide the spatial region into units which records how many times the trajectories passed in the unit time. It not only makes the storage space quite ineffective, but also can not maintain spatial data property. In this paper, a spatio-temporal aggregation index structure for moving object trajectory in constrained network is proposed. An extended B-tree node contains the information of timestamp and the aggregation values of trajectories with two directions. The network is divided into segments and then the spatial index structure is constructed. There are the leaf node and the non leaf node. The leaf node contains the aggregation values of moving object's trajectory and the pointer to the extended B-tree. And the non leaf node contains the MBR(Minimum Bounding Rectangle), MSAV(Max Segment Aggregation Value) and its segment ID. The proposed technique overcomes previous problems efficiently and makes it practicable finding moving object trajectory in the time interval. It improves the shortcoming of R-tree, and makes some improvement to the spatio-temporal data in query processing and storage.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.396-417
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• 2018

GR-tree and query aggregation techniques have been proposed for spatial query processing in conventional spatial query processing for wireless sensor networks. Although these spatial query processing techniques consider spatial query optimization, time query optimization is not taken into consideration. The index reorganization cost and communication cost for the parent sensor nodes increase the energy consumption that is required to ensure the most efficient operation in the wireless sensor node. This paper proposes itinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensor networks. This paper analyzes the performance of previous studies and IR-tree, which are the conventional spatial query processing techniques, with regard to the accuracy, energy consumption, and query processing time of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. This paper proves the superiority of the proposed IR-tree-based space-time indexing.